class_docstr: Beam search decoder for RNN-T model with biasing support.
  
  Args:
      model (RNNT): RNN-T model to use.
      blank (int): index of blank token in vocabulary.
      temperature (float, optional): temperature to apply to joint network output.
          Larger values yield more uniform samples. (Default: 1.0)
      hypo_sort_key (Callable[[Hypothesis], float] or None, optional): callable that computes a score
          for a given hypothesis to rank hypotheses by. If ``None``, defaults to callable that returns
          hypothesis score normalized by token sequence length. (Default: None)
      step_max_tokens (int, optional): maximum number of tokens to emit per input time step. (Default: 100)
      trie (list, optional): the prefix tree for TCPGen biasing
      biasing (bool, optional): If true, do biasing, otherwise use standard RNN-T support
code: def _get_trie_mask(self, trie):
      step_mask = torch.ones(len(self.model.char_list) + 1)
      step_mask[list(trie[0].keys())] = 0
      # step_mask[-1] = 0
      return step_mask
  
imports: from typing import Callable, Dict, List, Optional, Tuple
  import torch
  from torchaudio.models import RNNT
  from torchaudio.prototype.models.rnnt import TrieNode
label_desc: Use image node_id 3 for calling the RNNTBeamSearchBiasing obj's underlying member method code with example usage: obj._get_trie_mask(trie) and returns: step_mask